Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. - Raj Agrawal; Caroline Uhler; Tamara Broderick (ICML, 2018)

Data-dependent compression of random features for large-scale kernel approximation. - Raj Agrawal; Trevor Campbell; Jonathan H. Huggins; Tamara Broderick (AISTATS, 2019)

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery. - Raj Agrawal; Chandler Squires; Karren D. Yang; Karthikeyan Shanmugam; Caroline Uhler (AISTATS, 2019)

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. - Raj Agrawal; Brian L. Trippe; Jonathan H. Huggins; Tamara Broderick (ICML, 2019)

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. - Brian L. Trippe; Jonathan H. Huggins; Raj Agrawal; Tamara Broderick (ICML, 2019)

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. - Charles C. Margossian; Aki Vehtari; Daniel Simpson; Raj Agrawal (NeurIPS, 2020)

The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. - Thibaut Horel; Lorenzo Masoero; Raj Agrawal; Daria Roithmayr; Trevor Campbell (NeurIPS Datasets and Benchmarks, 2021)

Practical Methods for Scalable Bayesian and Causal Inference with Provable Quality Guarantees. - Raj Agrawal (2021)

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors. - Chandler Squires; Annie Yun; Eshaan Nichani; Raj Agrawal; Caroline Uhler (CLeaR, 2022)

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. - Raj Agrawal; Tamara Broderick (J. Mach. Learn. Res., 2023)

Automated Efficient Estimation using Monte Carlo Efficient Influence Functions. - Raj Agrawal; Sam Witty; Andy Zane; Elias Bingham (NeurIPS, 2024)